Download Development of a Novel Post-processing Treatment Planning

Technology in Cancer Research and Treatment
ISSN 1533-0346
Volume 7, Number 2, April 2008
©Adenine Press (2008)
Development of a Novel Post-processing Treatment
Planning Platform for 4D Radiotherapy
Lan Lin, Ph.D.1,2*
Chengyu Shi, Ph.D.1,2
Yaxi Liu, Ph.D.1,2
Gregory Swanson, M.D.3
Nikos Papanikolaou, Ph.D.1,2,3
www.tcrt.org
The aim of this study is to develop an Automatic Post-processing Tool for four-dimensional
(4D) treatment planning (APT4D) that enables the user to perform some necessary procedures related to 4D treatment planning, such as automated image registration, automatic
propagation of regions of interest, and dose distribution transformation. Demons-based deformable registrations were performed to map the moving phase images (such as the endinhalation phase or 0% phase) to the reference phase (typically the end-exhalation fixed
phase or 50% phase). Contours were automatically propagated into the moving phase
using the image registration results. The dose distribution of each moving phase was
transformed to the fixed phase and subsequently was summed as an average with equal
weighting factor. To validate the application of APT4D utility, the 4D computed tomography
(CT) images of a lung cancer patient and an abdominal cancer patient were acquired and
resorted into ten respiratory phases. 4D plans based on the 4D CT images were developed. The correlation coefficient ranged from 0.992 to 0.999 for the re-sampled deformed
moving phase image against the fixed phase image for the lung patient plan and from 0.977
to 0.999 for the abdominal patient plan. For all the organs, the match indices between the
manual contours and automatic contour propagation results were around 0.92 to 0.95. The
4D composite dose-volume histogram showed dosimetric reductions for liver and kidneys
in the high dose region. The APT4D adds automation, efficiency, and functionality, while
integrating the whole process of 4D treatment planning.
Department of Medical Physics
1
Cancer Therapy and Research Center
San Antonio, TX 78229, USA
Department of Radiology
2
Department of Radiation Oncology
3
University of Texas Health
Science Center at San Antonio
San Antonio, TX 78229, USA
Key words: 4D radiotherapy; Deformable registration; Treatment planning; Organ motion.
Introduction
The goal of modern radiotherapy treatment methodologies, such as intensitymodulated radiation therapy (IMRT) and stereotactic body radiation therapy
(SBRT), is to treat the tumor with more conformity, and possibly higher dose,
while sparing the normal structures and yielding improved local control and
survival (1). To achieve this goal, accurate patient setup and precise geometric
knowledge of the target volume are needed for treatment planning and treatment
delivery. Variations in patient setup can be minimized with the help of immobilization devices and patient positioning systems. However, internal organ
motion, both inter-fractionally and intra-fractionally, limits the dosimetric accuracy of the treatment and presents a challenge. Inter-fractional motion occurs
when the clinical target volume (CTV) changes on a daily basis and is a result of
tumor growth or shrinkage, weight loss or gain, daily bowel and bladder filling,
and radiation induced change of tissue. In clinical practice, the acquisition of
computed tomography (CT) images just prior to the treatment could be used to
adjust and verify the daily location of the CTV. Additionally, so-called “adaptive” radiotherapy offers the ability to account for the inter-fractional motion
Corresponding Author:
Lan Lin, Ph.D.
Email: [email protected]
*
Lin et al.
(2). Intra-fractional motion on the other hand, occurs while
the patient is being irradiated. It is caused by motion due to
the patient’s respiratory, skeletal muscular, cardiac and gastrointestinal systems. Of these systems, respiration-induced
motion is the most significant and one that has the highest
impact in radiation therapy. Respiratory motion can introduce significant errors in imaging (3-9), treatment planning
(9-11), and treatment delivery (12-15). In current radiotherapy treatment practice, the respiratory motion is typically
accounted for by adding uniform, population-based margins
to the CTV in order to obtain the internal target volume
(ITV) (1), often resulting in undesirably higher doses to unnecessarily exposed normal tissue.
The development of 4D imaging technology brings us to the
era of 4D radiotherapy, which allows us to capture and utilize the temporal changes in anatomy during the imaging,
planning, and delivery of radiotherapy. Acquiring 4D images (three spatial dimensions + phase) offers the capability
to track the patient’s respiratory motion with a reduction in
motion artifact. This facilitates the more accurate delivery
of treatment, especially in the abdomen and thorax. These
images enable 4D treatment planning, reflecting the realistic motion of the target that allows for reduction of the
ITV margin. In theory, the concept of 4D planning is intuitive; however, in practice it is very complex because of the
expanded amount of image data (10~20 times more image
data than a standard planning scan) (7). In principle, the
work flow of 4D planning involves automated image registration, automatic propagation of regions of interest (ROIs)
between phases and dose distribution transformation. Several investigators have published their efforts to implement
4D treatment planning through specific aspects of the 4D
planning procedure (16-19). However, a robust solution for
4D treatment planning is not yet widely available. The need
for the automation tools that can perform the necessary tasks
and reduce the workload is clear to all (20). Logistically,
performing all of the 4D planning tasks manually on such a
large volume of CT images would necessitate significant human interaction time. Although manual contouring slice by
slice is reliable, it is too time-consuming and labor-intensive
to be used routinely. To add the dose mathematically from
Tx
Planning
System
(TPS)
different phases, a process of dose transformation to the
same physical space and coordinate system is necessary.
In this study, we have constructed an Automatic Post-processing Tool for 4D treatment planning (APT4D) that allows deformable image-based registration. Additionally, we perform
automatic contour deformation and propagation between different imaging phases and 4D dose summation. The APT4D
is a post-planning tool since it depends on the output from a
commercial three-dimensional (3D) treatment planning system (TPS), requiring data such as contours and dose calculation matrix. In the following sections, the functionality and
clinical application of APT4D are described and tested for one
lung cancer patient and one abdominal cancer patient since
the respiratory motion gives a special challenge to the case
of thoracic and abdominal tumors compared to tumors at the
other sites of the body. It is important to note that APT4D is a
proof-of-principle at current stage rather than a clinical tool.
Materials And Methods
The graphical user interface (GUI) of APT4D was developed
to facilitate the generation of a 4D plan. The interface was
constructed using Matlab 6.5 (The MathWorks Inc., Natick,
MA, 01760, USA). The rigid registration algorithm and
non-rigid demons deformable registration algorithm were
implanted on the open source Insight Toolkit (ITK, www.
itk.org/) and implemented in C++ programming language.
The code was compiled as the executable file to be called in
APT4D. The data flow chart between APT4D and the TPS is
illustrated in Figure 1. The three dashed blocks represent, respectively, the input to APT4D, the functionality of APT4D,
and the output from APT4D. It is important to note that,
APT4D is not designed for a specific TPS although currently
APT4D has been tested and implemented in our institution in
two commercial TPS (Pinnacle3, Medical Systems, Milpitas,
CA; Corvus, North American Scientific, Chatsworth, CA).
As seen in Figure 1, the inputs into APT4D include 3D image
sets of different phases, one contour set manually delineated
on the fixed (reference) phase, and dose matrices calculated
for different phases. APT4D performs three major tasks:
INPUTS
APT4D: Processes
OUTPUTS
3D image sets of
different phases
Determine
transformation
Transformed 3D
image sets
Fixed phase
contour set
Transform
contour to other
moving phases
Contours for all
phases
3D plan doses of
different phases
Transform dose
to fixed phase
4D dose
distribution
Technology in Cancer Research & Treatment, Volume 7, Number 2, April 2008
Figure 1: Flow chart of integration of
APT4D and TPS.
Post-processing 4D Planning Platform
(i) non-rigid deformable registration, which determines the
transformation mapping between the fixed phase image and
each of the moving phases images; (ii) contour propagation,
which transforms contours of the fixed phase to all the moving phases; (iii) dose transformation, which maps voxelbased dose information from the moving phases back to the
fixed phase. The output of APT4D can be exported back to
TPS to finalize the plans for 4D delivery.
Image Acquisition
The images for a non-small cell lung cancer patient and a pancreatic caner patient were acquired on a multislice CT scanner (Lightspeed QX/I 4-slice scanner, GE Medical Systems,
Milwaukee, WI) in axial cine mode with quiet, free breathing.
During the scanning, respiratory motion information was acquired by monitoring the position of the anterior abdominal
surface (Real-time Position Management, RPM, Varian Medical Systems, Palo Alto, CA). The reconstructed axial images
and RPM respiratory data were retrospectively sorted into ten
respiratory phase bins using a commercial software package
(Advantage 4D CT application software package, GE Medical Systems, Milwaukee, WI). Data sets were labeled by the
percentage of the respiratory cycle, with 0% corresponding
to end-inhalation and 50% corresponding to end-exhalation.
The 50% phase was assigned as the fixed phase (reference
phase) in this study since this phase was generally considered
as the most reproducible phase (21). To reduce the memory
burden, images were down-sampled to a size of 256 × 256
with a pixel resolution of 1.96 × 1.96 mm2, while keeping the
same cranial-dorsal direction resolution of 2.5 mm.
Image Registration
The local deformable registration from the moving phase to
fixed phase was performed using the demons method. This
method was derived from the algorithm by Thirion (22). It
was inspired by the concept of optical flow and was initially
used in computer vision research. The algorithm was based
on the concept of diffusing models to perform image matching through the movement of a deformable grid through a
semi-permeable contour of an object surface in the other image. Each object surface in the image is defined by pixels
on its boundary contour, so called “demons”. The diffusion
model assumes the “demons” at each voxel from the fixed
image are applying forces that push the voxels of the moving
image m into the matched fixed image f. Iteratively, as the
model approaches the final convergence, the forces applied
gradually decreases. The displacement between the fixed
and moving images was derived by Eq. [1]: (22)
→
D=–
→
(m – f)∇f
→
||∇f ||2 + (m – f)2/k
[1]
→
where ∇ f represents the relationship between the neighboring points in the fixed image, k is the normalization factor
that accounts for the units imbalance between intensities and
→
gradients, D stands for the displacement or optical flow between the images, and the (m-f) means the differential force
of the interaction between the images. The above equation
was iteratively calculated with a Gaussian filter smoothing
the deformation field between iterations. Typically, a total of
150 iterations were to be sufficient in our implementation in
order to obtain optimal convergence.
The evaluation of the performance for image registration
is without any generally recognized “gold standard”. Both
subjective and objective evaluation methods were applied
to assess the behavior of the registration results. The subjective examination was qualitative. The transformed images were either exported back to the TPS for visual inspection or visually checked at APT4D. A subtraction image
between the fixed phase images and deformed moving
phase images was computed to assess the deformation and
registration operation. The quantitative evaluation involves
the similarity measurements between the two images. The
normalized correlation coefficient of two images at each
slice was calculated using Eq. [2]:
—
r=
—
∑ ∑ (Amn – A)(Bmn – B)
m n
—
—
(∑ ∑ (Amn – A)2(∑ ∑ (Bmn – B)2)
m n
[2]
m n
—
—
Here, A and B were the image intensities and A and B were
the mean intensity of the two images, respectively. If the two
images are identical, the correlation coefficient is equal to 1.
The closer the value to 1, the better the image registration
result. It is important to note that APT4D can be extended
to other registration algorithms, although currently only the
demons method is implemented.
Automatic Contour Propagation
The deformable registration-based contour propagation
method was applied in APT4D. The contouring of region
of interests (ROI) for the fixed phase was performed using
the Pinnacle3 TPS. A series of discrete transverse contours
that were represented by a list of vertices for ROIs at the
fixed phase were imported into APT4D. Since Matlab offers
the ability to handle images as a matrix operation, the planar
parallel contours were reconstructed as the 3D binary mask
image data that has zero value outside of the 3D outline of
ROIs. A user assigned image pixel value (also treated as organ identification) was assigned to each ROI for future identification. The imported ROIs were then transformed with
the displacement map from the image registration result into
other phases. Each new ROI was identified and extracted as
a contiguous chain of coordinates for the closed contour.
Technology in Cancer Research & Treatment, Volume 7, Number 2, April 2008
Lin et al.
The performance of the contour propagation was evaluated by
visually comparing the manual contours, and by quantitatively
calculating the matching index. The matching index was defined as the ratio of the intersection percentage of the volume
between the automatic contour propagation and the manual
contouring to the union of the two volumes, and is given in Eq.
[3]. If the contour propagation is identical to the manual contour, the match index is equal to 1. When the contour propagation does not have any intersection, the match index is 0.
match index =
contour propagatio ∩ manual contour
contour propagatio ∪ manual contour
[3]
Dose Transformation
The 3D dynamic MLC (DMLC) based IMRT treatment plans
were generated for 0% and 50% respiratory phase for both
patients. The lung cancer patient plan used five beams (each
at gantry angles: 130º, 180º, 230º, 280º, 330º). The seven
beams (each at gantry angles: 30º, 80º, 130º, 180º, 230º, 280º,
330º) were used for the pancreatic cancer patient plan. Each
phase of the IMRT plans produced deliverable step-andshoot beams at the completion of the optimization with the
same parameters. The dose matrix for the 0% phase plan
was transformed to the 50% phase via the matrices obtained
from the deformable image registration. The final combined
4D dose distribution was calculated on APT4D with an equal
weighting factor for each phase. Although, an equal weighting factor was utilized, different weighting factors may be
required for the optimization of 4D treatment planning.
Results
Image Registration
Figure 2 illustrates the subtraction (difference) images in axial view for CT volumes before and after image registration
at the 0% and 50% phases for the lung cancer patient. The
top row (a) shows the subtraction of images at different slices
before deformable registration. The bottom row (b) displays
the subtraction of images at different slices after deformable
registration. Arrows indicate the regions where differences
in pre and post registration are observed. The registration results for the abdominal cancer patient are shown in Figure 3.
A large anatomic change was apparent between the two peak
respiratory phases. Visually inspection of the performance
of deformable image registration indicates most of the motion was mapped correctly by the demons registration.
The quantitative validation for image registration is shown
in Figure 4 and Figure 5. Figure 4 plots the correlation coef-
(a)
slice 76
slice 80
slice 112
slice 135
slice 76
slice 80
slice 112
slice 135
(b)
Figure 2: Difference slices of images for
pre and post image registration of end-inhalation and end-exhalation 4D CT volumes for lung cancer patient. Top row:
subtraction images before image registration. Bottom row: subtraction images
after image registration. Arrows indicate
structures where differences are observed.
(a)
(b)
slice 61
slice 77
slice 91
slice 125
slice 61
slice 77
slice 91
slice 125
Technology in Cancer Research & Treatment, Volume 7, Number 2, April 2008
Figure 3: Difference slices of images for
pre and post image registration of end-inhalation and end-exhalation 4D CT volumes for abdominal cancer patient. Top
row: subtraction images before image registration. Bottom row: subtraction images
after image registration. Arrows indicate
structures where differences are observed.
Post-processing 4D Planning Platform
Figure 4: Correlation coefficient calculated before and after image registration of end-inhalation to end-exhalation 4D CT volumes for the lung cancer
patient. The dashed line represents the correlation coefficient before image
registration. The solid line with the square represents the correlation coefficient after image registration.
Figure 5: Correlation coefficient calculated before and after image registration of end-inhalation to end-exhalation 4D CT volumes for the abdominal
cancer patient. The dashed line with the diamond represents the correlation
coefficient before image registration. The solid line with the square represents the correlation coefficient after image registration.
ficient as a function of the image slice numbers before and
after deformable registration for the lung cancer patient.
Figure 5 shows the correlation coefficient for the abdominal
cancer patient. The correlation coefficient ranged from 0.992
to 0.999 for the resampled deformed moving phase image
against the fixed phase image for the lung patient and from
0.977 to 0.999 for the abdominal patient.
phase was deformed to the 0% phase. Figure 6a shows the
0% phase CT image of the lung patient with its associated
manual contours. The manual contours for the 50% phase
CT image are included for comparison in Figure 6b. Figure
7a compares the automatic contour propagation results with
the manual contours for the 0% phase superimposed on the
0% phase CT image. Figure 7b shows the manual contours
for the 0% phase and 50% phase overlaid on the 0% phase CT
image. It is evident that both the liver and kidneys have large
deformation due to respiratory motion. The implementation
of automatic contour propagation provides a much improved
and efficient way to complete the tedious contouring work
Contour Propagation
The 50% phase manual contour was used as the reference
contour. The 3D binary mask contour image at the 50%
(a)
(b)
Figure 6: (a) Comparison of automatic contour propagation
results (green) and the manual contour (red) for lung patient superimposed on the end-inhalation phase CT image. (b) Manual
contour on the end-inhalation phase (red) vs. manual contour on
the end-exhalation phase (blue).
(a)
(b)
Figure 7: (a) Comparison of automatic contour propagation results (green) and the manual contour (red) for abdominal patient
superimposed on the end-inhalation phase CT image. (b) Manual contour on the end-inhalation phase (red) vs. manual contour
on the end-exhalation phase (blue).
Technology in Cancer Research & Treatment, Volume 7, Number 2, April 2008
Lin et al.
for multiple scanning phases, even though some contouring
editing may still be necessary on some slices.
The match indices between the manual contours and automatic contour propagation results for the two extreme respiratory phases are summarized in Table I. The anatomic
change and deformation for the lung patient case is relatively
smaller than that for the abdominal patient case. The match
indices for the lungs between two manual contours on two
peak phases are around 89%. While the matching between
the manual contours for liver and kidneys are relatively poor
(less than 80%). For all the structures, the match indices
between the manual contours and automatic contour propagation results are increased at least 5% and up to 21%. This
increase demonstrated that the automatic contour propagation algorithm based on the deformable image registration
can match the contours to the real data very well.
Dose Transformation
Comparison of the dose volume histograms (DVH) among
the 0% phase plan, 50% phase plan and 4D plan are given in
Figure 8 for the PTV, cord and total lung. The DVHs for the
total lung were closely bunched. The 4D composite DVH
for PTV and spinal cord appeared to be around the middle of
each phase DVHs and tended to be closer to the 50% phase
DVH. The DVH for 0% and 50% phase plan and the 4D
plan for the PTV, liver and kidneys are shown in Figure 9.
The variation in liver and kidneys DVHs for the two extreme
Table I
Contour match indices for different organs.
Phase0 manual contour vs.
Phase5 manual contour
Phase0 manual contour vs.
automatic contour propagation
Left
lung
Right
lung
Liver
Left
kidney
Right
kidney
0.89
0.89
0.71
0.80
0.76
0.94
0.95
0.92
0.93
0.92
Figure 8: Dose volume histogram comparison of end-inhalation, end-exhalation phase plan, and the 4D plan for the PTV, cord, and lungs of patient.
phases was due to the change of the anatomy with respiratory
motion. For liver and kidney, the 4D composite DVHs were
around in the middle and showed the dosimetric reductions
in the high dose region. The 4D composite DVH was derived
from the summation over time and space through temporal
deformable image registration. It reflected a dynamic dose
distribution and assessed the dose delivered to each voxel of
the region of interest during the organ motion.
Discussion
In principle, the accuracy of 4D plans lies in the accuracy
of 4D image acquisition, the reproducibility of patient respiratory patterns, and the sorting process of respiratory signal to difference phases. Commercially available software
provides the automatic ability to sort images retrospectively
into the desired respiratory phases based on the temporal
correlation between image data acquisition and the abdominal surface motion from the RPM system. The post-processing can produce phase errors and may introduce a phase
shift. Rietzel et al. (23) reported that the RPM system inadequately models respiratory phase for about 30% of the
patients at their institution due to the irregularities in the
respiratory cycle. However, the phase shift itself does not
degrade the 4D plan (17). The variations between the actual
organ motion and acquired respiratory signal can degrade
the accuracy of 4D delivery. Breathing training techniques
had been proposed and may be helpful to retain the breathing regularity over the treatment course (24).
The residue uncertainty from the implementation of deformable registration needs to be taken into account for
the 4D specific uncertainties as no single non-rigid image
registration algorithm can be perfect for all anatomical
situations. Various deformable registration algorithms
have tradeoffs between the accuracy of organ deformation and the efficiency of computation. Currently, the
most prevailing image registration algorithms available
Figure 9: Dose volume histogram comparison of end-inhalation, end-exhalation phase plan, and the 4D plan for the PTV, liver, and kidney of patient.
Technology in Cancer Research & Treatment, Volume 7, Number 2, April 2008
Post-processing 4D Planning Platform
to the radiation oncology community include demons (25),
free form deformation (26, 27), finite element modeling (2830), thin-plate splines and B-splines (16, 31, 32), and diffeomorphic registration (17). An extensive and detailed comparison of different image registration methods is discussed
in the literature (26, 33). Although the demons registration
algorithm was used in our study, the inclusion of other methods to our software is very feasible and is the topic of ongoing research work from our group.
The study of automatic contour propagation is a complex
matter and an active research topic. The method presented in this paper is based on the deformable registration of
ROIs. Even though our patient study showed promise, more
investigation needs to be carried out. Visual verification of
the segmentation results is a necessary part of the validation process. Manual contour editing may be required if
the automatic contour propagation is not satisfactory. Other methods such as elastically deform contour and surface
model (34, 35) and automatic re-contouring using free from
registration technique (36, 37) can achieve the same goal
of automatic contour delineation in 4D planning. In this
study, we chose a simple and straight-forward algorithm to
implement the automatic contour propagation function to
demonstrate the utility of the APT4D tool.
The next step after the computation of the 4D plan is the
delivery of the plan. The 4D dynamic delivery for the conformal plan is a little easier than the IMRT plan due to the
complexity of the IMRT plan itself. If the body motion is
rigid and without deformation, the implementation is straight
forward. The delivery of 4D radiotherapy is beyond the
scope of this paper. Various factors need to be considered
including the respiratory patterns prediction, the optimization of the leaf sequence, the leaf response, and limitations in
acceleration and velocity, et cetera. Some pioneering works
have been published (18, 38-40), but further developments
and advancements are expected.
The efficiency in completing the 4D treatment plan is critical
to its applicability. For the case that requires deformable image registration, the total time for the entire 4D planning on
APT4D is about 15~20 minutes per phase depending on the
size of the image matrix and the number of ROIs. This time
does not include the time for the 3D plan in each phase and
the time for manual delineation.
Conclusion
An integrated platform for 4D radiotherapy called APT4D
has been developed for assisting the generation of a composite 4D treatment plan. The software platform includes an
automatic non-rigid image registration, automatic contour
mapping, and 4D dose combinations. A composite 4D treat-
ment plan can be generated from the phase specific treatment plans that are summed using appropriately weighting
factors. Preliminary results using the APT4D platform with
deformable image registration showed that the software
performs well in contour propagation as well as image and
dose deformation. The automation and functionality added
with APT4D have enabled us to improve the efficiency of
the 4D treatment planning process.
Acknowledgements
This project is sponsored in part by National Institutes of Health/National Library of Medicine grant
(1R01LM009362-01).
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Technology in Cancer Research & Treatment, Volume 7, Number 2, April 2008
Received: November 7, 2007; Revised: February 19, 2008;
Accepted: February 26, 2008